Top Data & Analytics Jobs

Lead and architect high-value healthcare analytics engagements, build reusable analytic frameworks (DBT), partner with product and engineering, mentor analysts, ensure analytic quality, and represent Arcadia externally to drive population health and value-based care outcomes.

Lead data science projects to optimize drug development using AI. Develop models for patient selection and clinical trial evaluations while collaborating with cross-functional teams to drive insights.

Lead and build the Mortgage Model Development Center's mortgage data analytics capability end-to-end. Design scalable analytics frameworks across MBS/EMBS, loan performance, Intex, and loan-level datasets. Develop Python-based tools for ingestion, transformation, QC, cohort analysis, feature engineering, and mortgage analytics. Partner with model development, validation, investment portfolio, technology, and business stakeholders. Establish data quality standards, documentation, controls, production workflows, and drive adoption of AI-enabled workflows for extraction, anomaly detection, and analytics acceleration.

The Manager of Regulatory Submissions will lead regulatory strategies and submissions for global authorities, ensuring regulatory alignment with business goals while collaborating with cross-functional teams on medical devices.

Develop and implement software solutions for automated trading systems, working with quantitative researchers on complex financial challenges.

Design, develop, and deploy software solutions for automated trading systems while collaborating with researchers on technology priorities.

As a PhD intern, you'll design, develop, and deploy software solutions for automated trading, collaborating with the Quantitative Research team.

Support risk and analytics efforts by performing SQL/Python-based analysis, monitoring portfolio health, building scalable reports, collaborating cross-functionally, and integrating AI tools to surface insights that inform product, risk, and growth decisions.

Partner with Total Rewards stakeholders to design and execute analytics that evaluate employee wellbeing and benefits program ROI. Combine and validate data from claims, utilization, and other sources, build forecasting and advanced-analytics models, and deliver dashboards and executive summaries. Lead project workstreams, mentor junior analysts, and ensure data quality, compliance, and actionable insights to inform benefits strategy.

Design, build, and productionize batch and streaming ETL/ELT pipelines and ML workflows on Azure Databricks. Implement Unity Catalog data products, MLflow model lifecycle controls, CI/CD for data/ML artifacts, and operational monitoring. Partner with cross-functional teams to modernize legacy workflows into scalable, production-ready solutions.

Lead data ownership and governance for Corporate Finance and Treasury: define business data meaning, drive data strategy execution, improve data quality and controls, manage delivery timelines, mitigate data risks across the lifecycle, and support analytics by governing data used in analytics platforms.

Lead data ownership and governance for wholesale credit risk. Define data strategies, ensure data quality, metadata and lineage, enable data products, mitigate data risks, and coordinate cross-functional delivery for analytics and reporting.

Drive card fraud strategy through data analysis, trend monitoring, and analytics-driven recommendations. Partner with product, technology, and operations to design, test, and implement detection and treatment solutions while balancing fraud mitigation, financial performance, customer experience, and operational efficiency.

Lead data ownership and governance to deliver curated, reusable data products. Partner across business, technology, analytics, and risk to define metadata, lineage, quality rules, and lifecycle management. Drive data integration, mitigate data risks, enable analytics/ML use cases, automate governance with AI/LLMs, and manage delivery, remediation, and stakeholder relationships to ensure scalable, discoverable, and enterprise-fit data products.

Develop, deploy, and monitor models and pattern-recognition algorithms to detect fraud across lending products. Write Python for analysis and modeling, query complex datasets with SQL, partner with Fraud Operations to refine features and reduce false positives/negatives, conduct ad hoc analyses, and communicate findings to support verification and underwriting improvements.

Design, build, and operate scalable data platforms and ETL/ELT pipelines using Python, SQL, Airflow, and AWS. Optimize data warehouses, ensure data quality and governance, implement CI/CD and infrastructure-as-code, and collaborate with cross-functional teams while mentoring engineers.

Own end-to-end analytical lifecycle to develop and optimize business, risk, and growth strategies. Monitor portfolio and macro trends, build and interpret ML-driven models, lead cross-functional launches, mentor junior analysts, and build AI-assisted workflows to improve decisioning and measurement across products and geographies.

Lead and develop a team delivering data conversion for customer implementations. Drive adoption of AI and automation, manage complex/at-risk migrations, track KPIs, and improve processes through experimentation and transformation.

Lead and scale the data engineering organization to build data products and architecture that power GTM (marketing and sales) strategy. Define technical strategy, enable reporting, analytics, data science, ML/AI workloads, and partner with IT, product, analytics, and GTM stakeholders to deliver production-grade data platforms and services.

Lead analytics workstreams for commercial payments fraud detection and operations. Translate business needs into scalable analytics, build dashboards and data products, evaluate and optimize models/rules, automate reporting pipelines, and deliver actionable insights to reduce fraud losses and improve operational effectiveness across stakeholders.

Curate, cleanse, validate, and maintain large US equity and options datasets; integrate fundamental and alternative data; build data pipelines for research and trading; perform statistical and ML analysis to identify market patterns; document data provenance and collaborate with engineers and traders to deploy production-grade models.

Develop and deploy advanced statistical and ML models on large-scale alternative datasets to generate investable signals, partner with investment teams, evaluate data quality, and drive novel data-driven research with commercial impact.

Develop, implement, calibrate, and validate statistical and machine learning models for risk management. Use Python, R, SAS, C++, SQL, MATLAB, Hadoop/NoSQL, and Unix/Linux. Provide analytical support and insights, document and present model development and validation results, recommend process improvements, and collaborate with peers, managers, and regulators to reduce model risk and meet regulatory standards.

Join Aladdin Financial Engineering as a quantitative modeler to research, develop, and deploy financial and portfolio risk models. Lead model governance workstreams, collaborate with researchers and engineers globally, present analytics to stakeholders and clients, and contribute to model validation, backtesting, and production deployment.

Lead analytics strategy and decision support across Product, Growth, Revenue, Operations, and Finance. Perform deep-dive analyses, design experiments, build predictive models and ML/AI prototypes, create trusted reporting and dashboards, ensure data quality, automate workflows, and partner with stakeholders to drive measurable business outcomes. Act as a primary analytics partner, prioritize work, communicate recommendations, and mentor analysts informally.